ABSTRACT Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder after Alzheimer's disease. Although PD is associated with Lewy body formation in the substantia nigra and other regions of the brain, the pathologic and metabolic alterations occurring during the onset and progression of PD have not been clearly defined. Despite a critical need for a reliable diagnostic marker for PD, there is currently no such biomarker that can be used accurately in clinical practice for establishing a definitive diagnosis of PD. The difficulty of identifying reliable biomarkers can be attributed to the variability of clinical samples, low abundance of proteins that are involved in PD pathogenesis and the lack of reproducibility in validating biomarker candidates. To overcome these limitations, we propose use of a large cerebrospinal fluid (CSF) cohort with greater statistical power for true discovery and deep proteome analysis to discover PD biomarkers that are involved in PD pathogenesis, but are present at low abundance. In addition, multiplexed sample analysis by isobaric tandem mass tagging (TMT) with a common reference for data normalization will ensure robust analytical precision of quantitative proteomic data for discovery from a larger set of samples. Moreover, additional proteomic analysis of substantia nigra will be used to select those biomarkers that show differential expression in CSF as well as substantia nigra. These discovery platforms will use a bioinformatics approach to select the most plausible candidates for targeted validation studies followed by an intensive validation of the discovered biomarker candidates. To achieve these goals, we propose three aims: Specific Aim 1: To discover proteins that are differentially expressed in patients with Parkinson's disease. We plan to carry out a quantitative proteomic analysis of CSF and substantia nigra samples from patients with PD and from controls by employing TMT-based multiplexing technology. With this approach, we expect to obtain a more comprehensive coverage of a larger number of proteins quantified across the analyzed samples. Specific Aim 2: To prioritize candidates based on an integrative analysis of alterations in CSF and substantia nigra. By integrating the expression changes in CSF and substantia nigra with a network approach that takes advantage of the known biological pathways that have been described in PD, our approach should be able to select reliable PD biomarker candidates for validation by targeted PRM experiments. Specific Aim 3: To validate candidate protein biomarkers in a larger cohort using targeted parallel reaction monitoring (PRM) mass spectrometry using CSF samples from a PD cohort at Johns Hopkins. Biomarkers that are selected by selection algorithms based on these PRM experiments will finally be confirmed with blinded PDBP CSF samples. Through the approaches outlined above, we expect to discover and validate reliable PD biomarkers in a reproducible fashion.